Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The perfect search engine is not enough: a study of orienteering behavior in directed search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What to do when search fails: finding information by association
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improved search engines and navigation preference in personal information management
ACM Transactions on Information Systems (TOIS)
Evaluating Long-Term Use of the Gnowsis Semantic Desktop for PIM
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
iMecho: an associative memory based desktop search system
Proceedings of the 18th ACM conference on Information and knowledge management
Beagle++: semantically enhanced searching and ranking on the desktop
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Evaluating an associative browsing model for personal information
Proceedings of the 20th ACM international conference on Information and knowledge management
ICWE'12 Proceedings of the 12th international conference on Current Trends in Web Engineering
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A typical collection of personal information contains many documents and mentions many concepts (e.g., person names, events, etc.). In this environment, associative browsing between these concepts and documents can be useful as a complement for search. Previous approaches in the area of semantic desktops aimed at addressing this task. However, they were not practical because they require tedious manual annotation by the user. In this work, we suggest a methodology and a prototype system for building a semantic representation of personal information based on click feedback from the user. We employed a feature-based model of associations between the concepts and documents. Our initial evaluation shows that the suggested semantic representation can play an important role in the known-item finding task and that the system can learn to predict such associations with a small amount of click data.